Recurrent Neural Network (RNN) Model Training System

A Recurrent Neural Network (RNN) Model Training System is a neural network training system that implements an RNN algorithm to solve an RNN training task.

• Context:
  • It can range from being a Uni-Directional RNN Training System to being a Bi-Directional RNN Training System.
• Example(s):
  • an LSTM Training System.
  • an RNN-based Language Model Trainer.
  • a Keras-based one. [1]
• Counter-Example(s):
  • a Convolutional NN Training System.
  • a Transformer NNet Training System.
• See: TensorFlow RNN.

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References

2017

• https://github.com/fchollet/keras/blob/master/README.md
  • QUOTE: Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow[2] or Theano[3]. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

    Use Keras if you need a deep learning library that:

    • Supports both convolutional networks and recurrent networks, as well as combinations of the two.

2015

• https://github.com/dennybritz/rnn-tutorial-rnnlm/blob/master/RNNLM.ipynb
  • QUOTE: Recurrent Neural Networks Tutorial, Part 2 – Implementing a Language Model RNN with Python, Numpy and Theano

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